物理引导的神经网络用于组织光学特性估计
Kian Chee Chong1, Manojit Pramanik2
1Nanyang Technological University, School of Chemistry, Chemical Engineering and Biotechnology, 62 Nanyang Drive, Singapore 637459, Singapore.
Biomedical optics express
|June 21, 2023
概括
研究人员开发了一种物理引导的神经网络 (PGNN),用于估计组织光学特性. 这种混合方法将物理原理与人工神经网络 (ANN) 结合起来,以提高生物成像应用中的准确性和通用性.
科学领域:
- 生物医学光学 生物医学光学
- 计算生物学是一种计算生物学.
- 医学成像医学成像
背景情况:
- 精确估计组织光学特性对于生物医学应用,如诊断和治疗至关重要.
- 传统方法依赖于基于物理的模型或数据驱动的机器学习,每个都有局限性.
- 将物理与机器学习相结合,为提高预测准确性提供了一个有希望的途径.
研究的目的:
- 开发和评估一种新的物理引导神经网络 (PGNN) 用于组织光学特性回归.
- 与纯人工神经网络 (ANN) 模型相比,研究PGNN的概括性.
- 评估PGNN在域内和域外数据集上的性能.
主要方法:
- 提出了一个物理引导的神经网络 (PGNN),将物理先验和约束集成到一个人工神经网络 (ANN).
- 该模型通过蒙特卡洛模拟生成的模拟单层组织样本进行训练和测试.
- 使用域内和域外测试数据集评估性能,以评估可概括性.
主要成果:
- 与纯粹的ANN模型相比,拟议的PGNN显示出更高的概括性.
- 在域内和域外测试数据集中,PGNN实现了更好的预测准确性和稳定性.
- 物理原理的整合显著提高了网络的概括能力.
结论:
- 物理引导的神经网络提供了一个强大的方法,用于准确和可概括地估计组织光学特性.
- 这种混合方法克服了纯粹基于物理或数据驱动的生物成像方法的局限性.
- 在生物医学诊断和治疗应用方面,PGNN具有显著的潜力.
相关概念视频
01:24Vision
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
01:26Visual System
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Once through the pupil, the light passes through the lens, a...